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Deep Learning Control for Sensors and IoT Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 12730

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Special Issue Editors

Prof. Dr. Zahir M. Hussain

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Guest Editor

School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: signal processing; wireless sensor networks; digital communications; image processing; electronic engineering; applied mathematics
Special Issues, Collections and Topics in MDPI journals

Dr. Katrina L. Neville

E-Mail Website
Guest Editor

School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interests: signal processing; artificial intelligence; digital communications

Special Issue Information

Dear Colleagues,

With the aid of digital signal processing (DSP) and artificial intelligence (AI), supported by recent developments in nanotechnology, wonderful progress has been achieved in modernizing the magnificent fields of linear and nonlinear control. The emerging field of deep learning (DL) is currently leading a revolution in analyzing complex systems in various fields. Deep learning is giving hope in creating novel strategies for handling challenging problems of linear and nonlinear control systems and their emerging applications arising from the growing integration of control with modern technologies such as IoT communications and electronic sensing. Knowing that systems such as wireless sensor networks (WSNs), which are the backbone of IoT, are confronted by limited storage, power, and computation capabilities, the task of control in such environments would be more challenging, as the ordinary complex DSP and DL techniques might have to be reconsidered. This Special Issue aims to present state-of-the-art control strategies in the fields of sensors, WSNs, and IoT-related applications.

Topics to be covered include, but are not limited to, the following areas in support of the control theory for sensors, wireless sensor networks (WSNs), and IoT applications:

Modelling and design of control systems in sensor networks.
Smart home control systems.
Control for IoT applications.
Deep-learning and/or adaptive DSP techniques in control.
Biomedical system modelling and control.
Adaptive control.
Energy-efficient control techniques and designs.
Low-cost hardware designs for control.
Computationally efficient control techniques.
Control in communication systems.
DL for real-time control.

Prof. Dr. Zahir M. Hussain
Dr. Katrina L. Neville
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

nonlinear control
robust control
real-time control
adaptive control
control systems
deep learning
signal processing
sensors
IoT
fuzzy control
wireless sensor networks
communication systems
control for biomedical applications

Published Papers (3 papers)

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Research

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16 pages, 1205 KiB

Open AccessArticle

A Comparative Investigation of Automatic Speech Recognition Platforms for Aphasia Assessment Batteries

by Seedahmed S. Mahmoud, Raphael F. Pallaud, Akshay Kumar, Serri Faisal, Yin Wang and Qiang Fang

Sensors 2023, 23(2), 857; https://doi.org/10.3390/s23020857 - 11 Jan 2023

Cited by 2 | Viewed by 2266

Abstract

The rehabilitation of aphasics is fundamentally based on the assessment of speech impairment. Developing methods for assessing speech impairment automatically is important due to the growing number of stroke cases each year. Traditionally, aphasia is assessed manually using one of the well-known assessment batteries, such as the Western Aphasia Battery (WAB), the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE), and the Boston Diagnostic Aphasia Examination (BDAE). In aphasia testing, a speech-language pathologist (SLP) administers multiple subtests to assess people with aphasia (PWA). The traditional assessment is a resource-intensive process that requires the presence of an SLP. Thus, automating the assessment of aphasia is essential. This paper evaluated and compared custom machine learning (ML) speech recognition algorithms against off-the-shelf platforms using healthy and aphasic speech datasets on the naming and repetition subtests of the aphasia battery. Convolutional neural networks (CNN) and linear discriminant analysis (LDA) are the customized ML algorithms, while Microsoft Azure and Google speech recognition are off-the-shelf platforms. The results of this study demonstrated that CNN-based speech recognition algorithms outperform LDA and off-the-shelf platforms. The ResNet-50 architecture of CNN yielded an accuracy of 99.64 ± 0.26% on the healthy dataset. Even though Microsoft Azure was not trained on the same healthy dataset, it still generated comparable results to the LDA and superior results to Google’s speech recognition platform. Full article

(This article belongs to the Special Issue Deep Learning Control for Sensors and IoT Applications)

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15 pages, 4522 KiB

Open AccessArticle

Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder

by Nuha A. S. Alwan and Zahir M. Hussain

Sensors 2022, 22(16), 5935; https://doi.org/10.3390/s22165935 - 09 Aug 2022

Cited by 7 | Viewed by 1769

Abstract

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system. Full article

(This article belongs to the Special Issue Deep Learning Control for Sensors and IoT Applications)

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Review

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26 pages, 1199 KiB

Open AccessReview

Control and Optimisation of Power Grids Using Smart Meter Data: A Review

by Zhiyi Chen, Ali Moradi Amani, Xinghuo Yu and Mahdi Jalili

Sensors 2023, 23(4), 2118; https://doi.org/10.3390/s23042118 - 13 Feb 2023

Cited by 25 | Viewed by 7509

Abstract

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay. Full article

(This article belongs to the Special Issue Deep Learning Control for Sensors and IoT Applications)

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